We keep the notation consistent with the notation used in the book of [1]}. n is the total layers (including input and out put layer) number of the network, \(a^k\)  is the pre-activation units in layer \(k\) , \(k_1\)  is the number of hidden units in hidden layer \(k\) , \(m\)  is the number of output units, and \(h(x)\)  stands for the non-linear activation function.
\(\begin{split}\mathbf {z}^0=\mathbf {x}\\\mathbf {a}^k=\mathbf {w}^k \mathbf {z}^{k-1}+\mathbf {b}^k\\\mathbf {z}^k=h(\mathbf {a}^k)\\\phi (\mathbf {x})=\mathbf {z}^n\end{split}\) 
